Chapter 11 - The t-test family - Debugged Codes
How to make a t-test significant - One-sample t-test - Two-sample t-tests - Effect size - Nonparametric t -test alternatives - More than two samples?

Chapter 12 - Correlations - Debugged Codes
Motivation and description of correlation - Covariance and correlation: formulas - Correlation matrix - Correlations in code - Assumptions of correlation - Simulating correlated data - Nonparametric correlations - Statistical significance - The subgroups correlation paradox - Cosine similarity

Chapter 13 - Confidence Intervals - Debugged Codes
Using and interpreting confidence intervals - Confidence interval vs. standard deviation - Analytical confidence intervals - Empirical confidence intervals - Confidence intervals & hypothesis testing

Chapter 14 - ANOVA - Debugged Codes
ANOVA: introduction and overview - ANOVA terminology - The math of the ANOVA - The ANOVA table - Post-hoc comparisons - Effect size - One-way ANOVA example. - One-way repeated-measures ANOVA - ANOVA residuals - The two-way ANOVA - "Types" of sums of squares - Sphericity and its corrections - Simulating data for ANOVAs - Nonparamatric ANOVA alternatives

Chapter 15 - Regression - Debugged Codes
Introduction to regression - Regression terminology and notation - The picture of regression - A simple example - Least-squares solution to the GLM - Evaluating regression models -

Chapter 15 - continuation ....
Standardizing regression coefficients - Regression in Python - Regression in R - Simulating data for regression - Assumptions of regression - Other regression model

Chapter 16 - Permutation Tests - Debugged Codes
When and why to use permutation testing? - Creating an empirical H0 distribution - Computing p-values - Permutation testing for means - Permutation testing for correlation - How many permutes? - What to permute? - Permutation testing vs. bootstrapping - Why not always use permutation testing?

Chapter 17 - Power and Sample Sizes - Debugged Codes
What is statistical power? - Estimating statistical power - How to increase statistical power - Estimating a required sample size - Computing statistical power in practice - A priori power vs. post-hoc power - Assumptions of power calculations

Chapter 18 - Biases - Debugged Codes
Science vs. the real world - Sources of biases - Conclusions

Chapter 19 - Data Communication - Debugged Codes
What is data communication? - Tell a story by crafting a data narrative - A few tips - Outlets for publishing data

Chapter 20 - Table of exercises - Debugged Codes
Table of exercises